Open Access
2014 Dynamic treatment regimes: Technical challenges and applications
Eric B. Laber, Daniel J. Lizotte, Min Qian, William E. Pelham, Susan A. Murphy
Electron. J. Statist. 8(1): 1225-1272 (2014). DOI: 10.1214/14-EJS920


Dynamic treatment regimes are of growing interest across the clinical sciences because these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. Formally, a dynamic treatment regime is a sequence of decision rules, one per stage of clinical intervention. Each decision rule maps up-to-date patient information to a recommended treatment. We briefly review a variety of approaches for using data to construct the decision rules. We then review a critical inferential challenge that results from nonregularity, which often arises in this area. In particular, nonregularity arises in inference for parameters in the optimal dynamic treatment regime; the asymptotic, limiting, distribution of estimators are sensitive to local perturbations. We propose and evaluate a locally consistent Adaptive Confidence Interval (ACI) for the parameters of the optimal dynamic treatment regime. We use data from the Adaptive Pharmacological and Behavioral Treatments for Children with ADHD Trial as an illustrative example. We conclude by highlighting and discussing emerging theoretical problems in this area.


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Eric B. Laber. Daniel J. Lizotte. Min Qian. William E. Pelham. Susan A. Murphy. "Dynamic treatment regimes: Technical challenges and applications." Electron. J. Statist. 8 (1) 1225 - 1272, 2014.


Published: 2014
First available in Project Euclid: 20 August 2014

zbMATH: 1298.62189
MathSciNet: MR3263118
Digital Object Identifier: 10.1214/14-EJS920

Keywords: adaptive confidence intervals , data-driven decision making , nonregular inference , Personalized medicine

Rights: Copyright © 2014 The Institute of Mathematical Statistics and the Bernoulli Society

Vol.8 • No. 1 • 2014
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